eprintid: 17466
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/74/66
datestamp: 2023-12-19 03:23:51
lastmod: 2023-12-19 03:23:51
status_changed: 2023-12-19 03:08:06
type: article
metadata_visibility: show
creators_name: Albashah, N.L.S.
creators_name: Rais, H.M.
title: Population Initialization Factor in Binary Multi-Objective Grey Wolf Optimization for Features Selection
ispublished: pub
keywords: Feature Selection; Multiobjective optimization, Classification results; Error rate; Feature selection methods; Features selection; Gray wolf optimizer; Gray wolves; Multi objective; Optimisations; Optimizers; Population initializations, Classification (of information)
note: cited By 0
abstract: Features selection methods not only reduce the dimensionality, but also improve significantly the classification results. In this study, the effect of the initialization population using the population factor has been explored. There are twenty wolves obtained by the population initialization method in binary multi-objective grey wolf optimization for features selection. There are two objectives function that will be minimized i.e. number of features and error rate. The proposed method has been compared with the previous study Binary Multi-Objective Grey Wolf Optimization (BMOGWO-S) using UCI datasets, oil and gas datasets. The results reflect that the proposed method outperforms all existence methods in terms of reducing feature numbers and error rates. © 2013 IEEE.
date: 2022
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141507067&doi=10.1109%2fACCESS.2022.3218056&partnerID=40&md5=869ce35d61e6247411a36c5a794b6e3e
id_number: 10.1109/ACCESS.2022.3218056
full_text_status: none
publication: IEEE Access
volume: 10
pagerange: 114942-114958
refereed: TRUE
issn: 21693536
citation:   Albashah, N.L.S. and Rais, H.M.  (2022) Population Initialization Factor in Binary Multi-Objective Grey Wolf Optimization for Features Selection.  IEEE Access, 10.  pp. 114942-114958.  ISSN 21693536